Senior Data Engineer: Scale Data Pipelines & ML Ops

TripleLift; Toronto (OH); On-site; USD 140,000 - 180,000; Full time

Job summary

TripleLift is hiring a Senior Data Engineer to expand and optimize data pipeline architecture and data flow across teams. You will build scalable pipelines and support multiple stakeholders including software engineers, product managers, BI analysts and data scientists.

The ideal candidate brings 5+ years of production data/ML experience, strong SQL and programming skills, and familiarity with Spark, Kafka, and cloud-based data tools. Remote-friendly and data-driven culture.

Qualifications

• Bachelor’s degree in Computer Science or a related technical field or equivalent professional experience.
• Minimum of 5 years of professional software engineering experience with a strong focus on data and ML pipelines in production at scale.
• Experience optimizing big data and ML pipelines using Spark and Kafka at scale.
• Knowledge of ML/AI ops and tools (MLFlow, Feast, Ray) is a plus.

Responsibilities

• Create and maintain scalable data pipeline architecture and systems.
• Assemble and curate large, complex datasets to meet business requirements.
• Identify and implement internal process improvements to automate and optimize data delivery.
• Build infrastructure for ETL using Spark, EMR, Kafka and other big data tech.
• Collaborate with product managers, engineers and analysts to support data infrastructure needs.

Skills

SQL

Python

Scala

Java

Education

Bachelor’s degree in Computer Science or related field

Tools

Spark

Kafka

OpenSearch

Druid

Clickhouse

InfluxDB

Kubernetes

ArgoCD

Job description

TripleLift is hiring a Senior Data Engineer to expand and optimize data pipeline architecture and data flow across teams. You will build scalable pipelines and support multiple stakeholders including software engineers, product managers, BI analysts and data scientists.

The ideal candidate brings 5+ years of production data/ML experience, strong SQL and programming skills, and familiarity with Spark, Kafka, and cloud-based data tools. Remote-friendly and data-driven culture.